Machine learning and deep learning applied to EEG and fNIRS for early autism spectrum disorder diagnosis: a systematic review

Abstract

Introduction: Autism Spectrum Disorder (ASD) presents considerable diagnostic challenges due to its heterogeneous nature and early developmental onset. In recent years, the convergence of noninvasive neuroimaging modalities such as Electroencephalography (EEG) and Functional Near Infrared Spectroscopy (fNIRS) with machine learning (ML) and deep learning (DL) techniques has opened new avenues for uncovering objective biomarkers of ASD. EEG offers millisecond level resolution of brain electrical activity, while fNIRS tracks hemodynamic responses tied to neuronal function, making the two methods complementary. This review aims to investigate the state of the art of the applications of EEG and fNIRS to ASD patients combined with ML and DL approaches. Methods: To this goal, Scopus and PubMed databases were searched, and following the PRISMA guidelines, 27 peer reviewed studies published between 2019 and 2024 were included in the survey. Results: The results showed consistent patterns across the studies, including alterations in neural oscillations and disruptions in connectivity within key brain regions related to social communication and cognition. However, a strong heterogeneity was assessed regarding probes montages, preprocessing workflows, and classification models employed, limiting the feasibility of a metanalysis. Discussion: The results demonstrated the potential of DL and ML algorithms applied to EEG and fNIRS signals for early ASD assessment, supporting the development of personalized intervention strategies grounded in robust neurophysiological evidence.